基于改进粒子群优化算法的包装生产工序优化排序研究OA

Aiming at the prominent problems in the mechanical processing process,such as chaotic process sequencing,low processing efficiency and high energy consumption,a multi-objective optimization model for mechanical processing processes was developed in combination with the process characteristics of customized production and strong process correlation in mechanical manufacturing.This model was intended to minimize the maximum flow time,reduce the total delay time,and lower the comprehensive production costs(including equipment energy consumption costs,work-in-progress inventory costs,and process switching costs).It incorporated a multi-dimensional process prioritization evaluation rule that integrates order urgency,output value coefficient,and process complexity.To enhance algorithm optimization performance,an Improve Particle Swarm Optimization(IPSO)algorithm was proposed,featuring dynamic inertia weight and adaptive learning strategies.The global exploration and local exploitation capabilities were balanced through segmented adjustment of the inertia weight,and the particle update mechanism was optimized by an adaptive acceleration coefficient based on population diversity,which effectively avoided the premature convergence of the algorithm.The corrugated box production line of a large packaging enterprise was taken as an example,and the simulation experiments were carried out in MATLAB,with the results compared and verified against the genetic algorithm and standard PSO algorithm.The research results show that the proposed model and algorithm can significantly improve the rationality of process sequencing for packaging production.In comparison with the GA algorithm,it shortens the production cycle by 12.3％,reduces the total delay time by 21.7％,and cuts down the comprehensive cost by 8.9％.The excellent performance in experiments validates the practicality of model and the superiority of algorithm,offering a reference for optimizing production scheduling of packaging enterprises.